1. Field of Art
The present disclosure generally relates to determining safety risks of users of a network system, and more specifically to using natural language processing and various types of classifiers to determine the safety risks.
2. Description of the Related Art
Natural language processing techniques can be used to determine characteristics of a string of text. For example, a sentence including the words “cats” and “dogs” is likely to be relevant to the topic of pets. However, the sentence “it's raining cats and dogs” includes both of those words, but is not relevant to the topic of pets. Instead, the sentence refers to an English idiom. Thus, it is challenging to determine the topic of a sample of text due variations in context.
In a system, providers provide services to users, for example, the provider uses a vehicle to transport the user for a trip. If the users perceive the providers as unsafe (e.g., driving recklessly or being confrontational), the users may stop using the system's services. Users can provide textual feedback to the system to report incidences of unsafe behavior, but automatic and systematic analysis of this textual feedback to determine provider characteristics and level of safety risks has proven challenging. Without such an automated way, subjective human techniques may be applied that are expensive to implement and rely on the subjective considerations of individual reviewers. It would be desirable for the system to automatically analyze the textual feedback to determine whether providers are safe or unsafe, and for the system to handle submissions of the textual feedback at a large scale.